class_docstr: Densenet-BC model class, based on
  `"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf>`
  Args:
      growth_rate (int) - how many filters to add each layer (`k` in paper)
      block_config (list of 3 or 4 ints) - how many layers in each pooling block
      num_init_features (int) - the number of filters to learn in the first convolution layer
      bn_size (int) - multiplicative factor for number of bottle neck layers
          (i.e. bn_size * k features in the bottleneck layer)
      drop_rate (float) - dropout rate after each dense layer
      num_classes (int) - number of classification classes
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code: def __init__(
      self,
      growth_rate=12,
      block_config=(16, 16, 16),
      compression=0.5,
      num_init_features=24,
      bn_size=4,
      drop_rate=0,
      avgpool_size=8,
      num_classes=10,
  ):
      super(DenseNet, self).__init__()
      assert (
          0 < compression <= 1
      ), "compression of densenet should be between 0 and 1"
      self.avgpool_size = avgpool_size
  
      # First convolution
      self.features = nn.Sequential(
          OrderedDict(
              [
                  (
                      "conv0",
                      nn.Conv2d(
                          3,
                          num_init_features,
                          kernel_size=3,
                          stride=1,
                          padding=1,
                          bias=False,
                      ),
                  )
              ]
          )
      )
  
      # Each denseblock
      num_features = num_init_features
      for i, num_layers in enumerate(block_config):
          block = _DenseBlock(
              num_layers=num_layers,
              num_input_features=num_features,
              bn_size=bn_size,
              growth_rate=growth_rate,
              drop_rate=drop_rate,
          )
          self.features.add_module("denseblock%d" % (i + 1), block)
          num_features = num_features + num_layers * growth_rate
          if i != len(block_config) - 1:
              trans = _Transition(
                  num_input_features=num_features,
                  num_output_features=int(num_features * compression),
              )
              self.features.add_module("transition%d" % (i + 1), trans)
              num_features = int(num_features * compression)
  
      # Final batch norm
      self.features.add_module(
          "norm_final", nn.BatchNorm2d(num_features)
      )
  
      # Linear layer
      self.classifier = nn.Linear(num_features, num_classes)
  
file_path: gpytorch/examples/06_PyTorch_NN_Integration_DKL/densenet.py
imports: from collections import OrderedDict
  import torch
  import torch.nn
  import torch.nn.functional
label_desc: Use image node_id 1 to create a new DenseNet object from inherited base classes: nn with example: obj = DenseNet(growth_rate, block_config, compression, num_init_features, bn_size, drop_rate, avgpool_size, num_classes)